TY - JOUR
T1 - Personalized Model to Predict Keratoconus Progression From Demographic, Topographic, and Genetic Data
AU - Maile, Howard P.
AU - Li, Ji Peng Olivia
AU - Fortune, Mary D.
AU - Royston, Patrick
AU - Leucci, Marcello T.
AU - Moghul, Ismail
AU - Szabo, Anita
AU - Balaskas, Konstantinos
AU - Allan, Bruce D.
AU - Hardcastle, Alison J.
AU - Hysi, Pirro
AU - Pontikos, Nikolas
AU - Tuft, Stephen J.
AU - Gore, Daniel M.
N1 - Funding Information:
Funding/Support: Howard P. Maile is funded by a Moorfields Eye Charity PhD Studentship (GR001147). Nikolas Pontikos is funded by a Moorfields Eye Charity Career Development Award (R190031A). Moorfields Eye Charity is supported in part by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology. Stephen J. Tuft, Bruce D. Allan, and Daniel M. Gore acknowledge that a proportion of their financial support is from the Department of Health through the award made by the National Institute for Health Research to Moorfields Eye Hospital NHS Foundation Trust and University College London Institute of Ophthalmology for a Specialist Biomedical Research Centre for Ophthalmology.
Publisher Copyright:
© 2022 Elsevier Inc.
PY - 2022/8
Y1 - 2022/8
N2 - PURPOSE: To generate a prognostic model to predict keratoconus progression to corneal crosslinking (CXL). DESIGN: Retrospective cohort study. METHODS: We recruited 5025 patients (9341 eyes) with early keratoconus between January 2011 and November 2020. Genetic data from 926 patients were available. We investigated both keratometry or CXL as end points for progression and used the Royston-Parmar method on the proportional hazards scale to generate a prognostic model. We calculated hazard ratios (HRs) for each significant covariate, with explained variation and discrimination, and performed internal-external cross validation by geographic regions. RESULTS: After exclusions, model fitting comprised 8701 eyes, of which 3232 underwent CXL. For early keratoconus, CXL provided a more robust prognostic model than keratometric progression. The final model explained 33% of the variation in time to event: age HR (95% CI) 0.9 (0.90-0.91), maximum anterior keratometry 1.08 (1.07-1.09), and minimum corneal thickness 0.95 (0.93-0.96) as significant covariates. Single-nucleotide polymorphisms (SNPs) associated with keratoconus (n=28) did not significantly contribute to the model. The predicted time-to-event curves closely followed the observed curves during internal-external validation. Differences in discrimination between geographic regions was low, suggesting the model maintained its predictive ability. CONCLUSIONS: A prognostic model to predict keratoconus progression could aid patient empowerment, triage, and service provision. Age at presentation is the most significant predictor of progression risk. Candidate SNPs associated with keratoconus do not contribute to progression risk.
AB - PURPOSE: To generate a prognostic model to predict keratoconus progression to corneal crosslinking (CXL). DESIGN: Retrospective cohort study. METHODS: We recruited 5025 patients (9341 eyes) with early keratoconus between January 2011 and November 2020. Genetic data from 926 patients were available. We investigated both keratometry or CXL as end points for progression and used the Royston-Parmar method on the proportional hazards scale to generate a prognostic model. We calculated hazard ratios (HRs) for each significant covariate, with explained variation and discrimination, and performed internal-external cross validation by geographic regions. RESULTS: After exclusions, model fitting comprised 8701 eyes, of which 3232 underwent CXL. For early keratoconus, CXL provided a more robust prognostic model than keratometric progression. The final model explained 33% of the variation in time to event: age HR (95% CI) 0.9 (0.90-0.91), maximum anterior keratometry 1.08 (1.07-1.09), and minimum corneal thickness 0.95 (0.93-0.96) as significant covariates. Single-nucleotide polymorphisms (SNPs) associated with keratoconus (n=28) did not significantly contribute to the model. The predicted time-to-event curves closely followed the observed curves during internal-external validation. Differences in discrimination between geographic regions was low, suggesting the model maintained its predictive ability. CONCLUSIONS: A prognostic model to predict keratoconus progression could aid patient empowerment, triage, and service provision. Age at presentation is the most significant predictor of progression risk. Candidate SNPs associated with keratoconus do not contribute to progression risk.
UR - http://www.scopus.com/inward/record.url?scp=85131383999&partnerID=8YFLogxK
U2 - 10.1016/j.ajo.2022.04.004
DO - 10.1016/j.ajo.2022.04.004
M3 - Article
C2 - 35469790
AN - SCOPUS:85131383999
SN - 0002-9394
VL - 240
SP - 321
EP - 329
JO - American journal of ophthalmology
JF - American journal of ophthalmology
ER -